A sample that accurately reflects the characteristics of the population as a whole.

• A. Representative sample
• B. Sampling bias
• C. Random sampling
• D. Cross-sectional design

Answer: (A)

A sample that mirrors the population is crucial for generalizing study results. A representative sample means its characteristics—such as age, gender, ethnicity, and other relevant factors—match the population from which it’s drawn, so findings can be meaningfully extended beyond the participants studied. This alignment supports external validity, the ability to apply results to the broader group.

Sampling bias, by contrast, happens when the selection process skews which individuals are included, so the sample’s traits don’t reflect the population and generalization becomes suspect. Random sampling is a method aimed at achieving representativeness by giving everyone an equal chance to be included, but it doesn’t guarantee perfection in every real-world study, especially with smaller samples. Cross-sectional design refers to collecting data at a single point in time, which is about timing of data collection rather than how well the sample represents the population.